Soft robot octopus crawlers learn diverse adaptable arm movements
Diverse and Adaptable Arm Coordination for Octopus-Crawling via Diffusion-Based Uncertainty-Aware Optimization
RoboticsMachine Learning
Summary
Controlling an octopus-like soft robot with many arms is hard because there are many ways to move, and the robot must adapt to obstacles or broken parts. The authors created a new method, called DUO, that teaches a simulated robot how to crawl in many different ways without needing example movements. This variety helps the robot adjust when conditions change, like when some arms don’t work properly. Their approach also lets the robot quickly update its movements without starting from scratch.
What this means in practice
- •For soft robotics engineers: Develop soft multi-arm crawling robots that can adapt to damage or changing terrain with diverse learned movement modes.
- •For robot control developers: Use DUO to optimize and adapt robot controllers in simulations with uncertain, contact-rich environments without requiring demonstration data.
Authors
Seung Hyun Kim, Heng-Sheng Chang, Kimia Kazemi, Prashant Mehta, Mattia Gazzola
Abstract
Octopus crawling motivates soft robots that exploit redundancy, yet discovering and organizing diverse coordination modes for adaptation remains challenging. To address this, we introduce a Diffusion-based Uncertainty-aware Optimization (DUO) algorithm that learns demonstration-free crawling controllers for a simulated, muscle-actuated CyberOctopus. This work represents the first application of diffusion-based control to soft multi-arm robots in contact-rich simulations. By embedding a variety of locomotion behaviors within a shared control distribution, this approach enables the simulated octopus to navigate dynamic physical constraints, demonstrating that learned coordination diversity inherently facilitates robust adaptation. The main contributions include: (i) a symmetry-structured policy representation that folds radially equivalent controllers into a canonical directional sector, (ii) an online black-box optimization strategy, the DUO algorithm, that discovers and retains diverse coordination modes, and (iii) a control editing technique that adapts existing controllers to novel actuator constraints without retraining. These results show how learned coordination diversity makes motor abundance a practical resource for adaptation in soft multi-arm robots.